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Investigation of pedestrian crashes using multiple correspondence analysis in India.
Sivasankaran, Sathish Kumar; Balasubramanian, Venkatesh.
Afiliação
  • Sivasankaran SK; RBG Lab, Department of Engineering Design, IIT Madras, Chennai, India.
  • Balasubramanian V; RBG Lab, Department of Engineering Design, IIT Madras, Chennai, India.
Int J Inj Contr Saf Promot ; 27(2): 144-155, 2020 Jun.
Article em En | MEDLINE | ID: mdl-31709899
ABSTRACT
Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009-2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: Asia Idioma: En Ano de publicação: 2020 Tipo de documento: Article